Leading retailers - like Walmart, Stop & Shop, and Home Depot - are enhancing their payment and fraud detection systems, using artificial intelligence that learns transaction norms and infers risk from the context of each transaction.
Barclays is a UK bank ranked 20th on S&P Global’s list of the top 100 banks. Like other top banks, Barclays has forayed into AI for a variety of use-cases. The bank seems to work with AI vendors more than it builds AI applications in-house, which aligns with the general trend of AI adoption in financial services: 68% of the AI products we researched as part of our AI Opportunity Landscape research in financial services were bought from vendors.
UBS is a Swiss multinational investment banking and financial services company ranked 30th on S&P Global’s list of the top 100 banks. In addition to investment banking and wealth management, the company is looking to improve its tech stack through several AI projects.
Morgan Stanley is a US financial institution known mostly for its financial advisory services. According to our AI Opportunity Landscape research in financial services, approximately 10% of AI vendor products in the industry are wealth management solutions, and 4% are asset management solutions.
Progressive is one of the largest auto insurers in the US. The company has been experimenting with AI since the middle of the 2010s, with customer-facing applications that update insurance premiums based on driving habits and answer questions in a chat window. In this article, we discuss both of these AI use-cases. More specifically:
The top 100 global banks, including Goldman Sachs, are beginning to take AI strategies very seriously.
The retail industry collects massive amounts of data every day, and this makes its key processes ripe for automation with machine learning. Along with the manufacturing sector, the retail industry likely stands to benefit the most from one particular AI technique in the next few years: machine vision, also known as computer vision.
Many large insurers are finding ways to digitize parts of their business process in preparation for future projects involving machine learning. This is especially true in claims processing, which could become faster and less error-prone if claims adjusters did not have to search through large amounts of data or paper documents manually.
What is the state of AI in business today - and what do mid-market business leaders need to know about AI now?
Several key insurance carriers began to experiment with AI in the last decade, including Progressive, All-State, and State Farm. Although not as large as the banking and retail industries, the AI vendor landscape in insurance is growing.
The retail and eCommerce sectors were among the first to adopt natural language processing (NLP) in the enterprise, particularly by way of chatbots and conversational interfaces. In this article, we cover three ways retailers can use NLP to automate business processes and offer the customer a better experience. We also give examples of AI vendors that offer this technology and describe their products. The NLP capabilities we discuss include:
HSBC Holdings is a multinational banking and financial services holding company and is ranked 99th on the Fortune 500 list. The bank has worked with multiple AI vendors and provided evidence of success that other top banks lack. According to our AI Opportunity Landscape research on how the top global banks are using AI, besides Deutsche Bank, HSBC is the European bank with the most AI initiatives. HSBC's AI initiatives account for 12.5% of the AI initiatives at the European banks in our analysis.
The auto-lending industry stands to benefit from artificial intelligence in much the same way as insurance companies, particularly when it comes to underwriting and risk management. According to Deloitte, nearly $500 billion in new loans and leases are originated annually, and 86% of new car purchases rely on borrowed money. In this article, we discuss how AI startups aim to facilitate different processes within the auto-lending industry, looking to two well-funded startups as examples of what's possible in the space:
The retail industry could be losing nearly $1 trillion in sales annually due to business process errors that could be automated by AI, such as restocking in eCommerce. In this article, we discuss the top 3 most well-funded AI startups selling to the retail industry and how their solutions could help retailers and eCommerce sites save money lost to fraud and increase revenue through customer analytics.
Signifyd - Retail and eCommerce Fraud Detection
Signifyd is the most well-funded AI startup in the fraud detection industry for retail and eCommerce, having raised $180 million. They were founded in August 2011 and specialize in fraud detection for retail and eCommerce companies. Their most prominent offering is called “guaranteed fraud detection,” and it likely uses anomaly detection technology to recognize fraudulent transactions and prevent chargebacks. The offering was originally announced exclusively for the Magento eCommerce platform in 2017.
Many of the largest US banks, including Bank of America, are starting to automate many of their business processes with AI. These include ACH payments and more specific processes such as order to cash. Bank of America has been investing seriously in AI and machine learning since at least 2017 and continues to research ways they can take advantage of AI in the future.
Much of the discussion surrounding AI in banking is focused on retail banks, with applications such as chatbots and payment fraud detection. But there exist AI capabilities that stand to benefit investment banking clients specifically as wealth management departments begin to adopt them.
Many AI vendor companies offer AI-enabled products and services for pushing more and more products in front of customers. That said, it is not always clear how these solutions determine which products to advertise to which customers. Retailers and other businesses should consider what they need to do to prepare their enterprise for one of these solutions and familiarize themselves with how AI recommendations are built and trained.
Many large banks and financial institutions are beginning to digitize parts of their business processes to prepare for future initiatives in automation and machine learning. This is particularly true with loan processing. These functions could become faster and more accurate if they use digitized data that is more easily accessible than paper documents.
In this brief overview, we run through several use-cases for voice recognition software in the healthcare industry. Voice recognition software, built on natural language processing (NLP) algorithms, primarily finds a home in the doctor's office. Physicians use it to dictate their notes into their healthcare network's system or update patient electronic medical records (EMR).
Wells Fargo has begun a number of AI initiatives, some they've created in-house and some they've created with help from vendors. In this article, we detail the following AI initiatives at Wells Fargo:
It can be difficult for financial institutions to keep up with the rapid changes in the digital marketing and advertising landscape. There are numerous factors which are susceptible to change, and they all have an effect on how useful certain marketing strategies are. As the internet and advertising evolve, some companies may find it important to consider an automated solution to driving efficiency in marketing.
A chatbot is a prominent type of AI application used by a variety of businesses for resolving issues related to conversations between the business and its customers, clients, staff, or business partners.
Money laundering is a financial fraud method unique to what AI vendors and other solutions providers refer to in their “fraud detection” offerings. This is because the technique centers on making fraudulent money transfers that appear to be validated by two willing parties.
Several high-profile banks are leveraging anomaly detection solutions for fraud and anti-money laundering. While some banks and AI firms provide information on how their solution works or how their chosen solution worked for them, it can be hard to determine which ones are successful today.
Many financial institutions are experimenting with chatbots both for general customer service and for offering new and better financial services to their customers. In addition to banks and insurance companies, other types of financial services companies can benefit from this type of application as well. Financial customers can now check the status of their loan applications and stock portfolios and request refunds using AI-powered conversational interfaces.
According to Fortune, JPMorgan Chase is the largest bank in the U.S. and controls over $2 trillion in total assets. In this article, we detail the types of AI research JPMorgan is doing as well as how they are likely to be using their applied AI applications.
The military is always looking for ways to innovate its technology for weapons and vehicles, and it follows that AI and ML would become part of that work in the current decade. Currently, the Army is testing autonomous vehicles and aircraft for battlefield use. However, most AI applications for these vehicles do not have clearance to operate the weapons attached to them.
Banks and other financial institutions can be tight-lipped about how they implement AI technologies within their businesses. Citi, however, has been relatively open about their current AI initiatives. Since 2017, they have published press releases and other announcements of AI initiatives that are both internal and customer-facing.
Predictive analytics is perhaps one of the most common AI applications used by financial institutions, banks, insurance companies, and healthcare companies. This type of software allows business leaders across these industries to plan for the most probable outcomes in business areas such as credit, loans, and patient health. Predictive analytics software could make predictions about future business events based on typical company experience using historical enterprise data.
Many of the top Fortune 500 retailers have begun using AI and ML to solve business problems for various departments. Walmart and Costco share one in grocery stocking, which includes the freshness and condition of the products along with timing the restocks for peak hours.